title: Estimating mood variation from MPF of EMG during walking.
abstract: The information on the mood included in behavior is classified into nonverbal information, and is included in behavior without necessarily being based on the intention of an agent. Consequently, it is considered that we can estimate the mood from the measurement of the behavior. In this work, we estimate the mood from the surface electromyogram (EMG) information of the muscles of the upper limb during walking. Identification of emotion and mood using EMG information has been done with a variety of methods until now. In addition, it is known that human walking includes information that is specific to the individual and be affected by mood. Therefore, it is thought that the EMG analysis of walking is effective in the identification of human mood. In this work, we made a subject walk in the various mood states and answer psychological tests that measure the mood. We use two types of tasks (music listening and numerical calculation) for evoking different moods. Statistical features of EMG signals are calculated using Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). These statistical features are related with psychological test scores, using regression analysis. In this paper, we have shown the statistical significance of the linear model to predict the variation of mood based on the information on the variation in MPF of EMG data of the muscles of the upper limb during walking with different moods. This shows the validity of such a mapping. However, since the interpretability of the model is still low, it cannot be said that the model is able to accurately represent the mood variation. Creating a model with high accuracy is a key issue in the future.
authors.forename: Yuta
authors.initials: Y
authors.lastname: Kinase
authors.forename: Gentiane
authors.initials: G
authors.lastname: Venture
mesh_terms: D000339:Affect; D004576:Electromyography; D005583:Fourier Analysis; D006801:Humans; D016014:Linear Models; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D018482:Muscle, Skeletal / Q000502:physiology*; D009146:Music; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D012984:Software; D016138:Walking / Q000502:physiology*